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How to deliver relevant customer experiences

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How to deliver relevant customer experiences

Ross Moses, Senior Director of Analytics and Insights at the United States Soccer Federation, saw a need to re-evaluate how the organization interacted with its customer base. Despite the vast amount of digital resources available, he found it was failing to offer fans engaging experiences.

“We were sitting on an outdated digital infrastructure — there wasn’t much there,” Moses said in his presentation at our MarTech conference. “We had an outdated website, no mobile app, and a lot of manual tasks that we were using to track our customers. So, we knew that we had to invest heavily in this space.”

Getting your brand’s digital infrastructure in place is important, but it’s nothing without establishing strong customer connections. To do so, U.S. Soccer needed strategies and tools to better identify its customers — who they were and where they were coming from.

“The next challenge was identifying our fans, getting the 360-degree view of the customer,” Moses said. “This tends to be the Holy Grail, not just for us, but for a lot of businesses out there. Putting that in place would allow us to have that business intelligence to have more efficient feedback loops, and then enable personalization.”

Moses and his marketing team worked with customer data platform Treasure Data to develop a robust customer experience strategy. Here are some of the insights they gained in the process.

Use automation to personalize experiences

Moses highlighted U.S. Soccer’s loyalty program, which rewarded customers based on their interactions with the program. Using a CDP framework, The organization was able to personalize each customer’s experience with automated messaging that fit their group.

CDP automation to personalize customer experience
Source: Treasure Data

“This is our loyalty program known as U.S. Soccer Insiders,” said Moses. “Based upon the tier that you’re in, from the standard tier up to VIP, you’re getting a different message.”

He added, “This illustrates how once you have that segment created in a CDP, you’re sending it somewhere else. And then you’re using those audiences to program step-by-step journeys.”

Brands should remember that CDPs aren’t designed to fit within every organization’s framework — some aren’t going to meet your automation or personalization needs exactly. However, many allow users to develop custom solutions.

“There are a lot of pre-built connectors [in CDPs],” he said. “But if there’s something that not off-the-shelf, you can develop on top of it. So, if there’s some obscure data source, you can build those pipelines so that it comes in a batch or stream.”


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Gather relevant business intelligence

Moses and his team extracted the relevant customer information from all of their platforms using a CDP, pulling in relevant information such as average annual spending, renewals, etc. They used this data to make better business decisions when choosing U.S. Soccer’s match locations.

U.S. Soccer business intelligence gained from CDP
Source: Treasure Data and U.S. Soccer

“There’s a lot that goes into picking where we take the team,” he said. “It’s not all based upon where the customers are. There’s a lot of other factors, like the opposing team, or how long it’s been since we’ve been there.”

He added, “This information helps us understand where customers are coming from and where we think there might be an opportunity.”

Pulling in this valuable business intelligence from many customer sources is critical for brands wanting to develop a sustainable customer experience strategy.

Develop a future vision

Using the insights gathered from its audience, U.S. Soccer made a new game plan for delivering enhanced customer experiences. Knowing what fans wanted out of its digital assets, the organization developed new ways of connecting with them.

future customer experience strategy
Source: Treasure Data

One of the more popular upgrades allowed customers to predict the starting 11 players, which also extended to other aspects of the game.

“Soccer fans love to debate who should be in the starting lineup, or who should even be called into the roster for a specific game,” Moses said. “This is a way to build some community and discussion around this topic. Within the app, we allow people to easily create ana field map and then share it on social, continuing the conversation.”

He added, “A lot of the personalization we’re doing is in advertising or email marketing, but we need to bring this into our inbound experiences.”

Gaining a full view of customers and extracting insights from their journeys serve as foundations for future strategies. But to be sustainable, marketers must practice proper data governance to keep everything in line. This is where CDPs often work well with data management platforms (DMPs), informing the latter to create more relevant audience experiences.

“Our CDP is our master data [source], but we need to do more in terms of bringing in those other data sources and getting that holistic view,” Moses said. “We need to make sure that we have that data mastered so that we have the utmost confidence in our analytics and personalization.”

About The Author

1640828540 338 Why brands must embrace responsible marketing practices

Corey Patterson is an Editor for MarTech and Search Engine Land. With a background in SEO, content marketing, and journalism, he covers SEO and PPC to help marketers improve their campaigns.


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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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